The multi-robot collaborative SLAM scheme solves the issues of high individual cost, global error accumulation, excessive concentration of calculation and risk under a single robot SLAM(Simultaneous Localization and Mapping) scheme, and has strong robustness and stability, which is a research hotspot when it comes to large-scale environmental mapping requirements. This study analyzes the history of this field's growth and introduces the multi-robot collaborative SLAM's fusion method and architecture. The current collaborative SLAM methods are arranged from the viewpoint of machine learning classification. The future development of deep learning, semantic maps, and multi-robot VSLAM, along with other significant multi-robot SLAM development directions, are also introduced at this time.